tequila_test.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tequila as tq"
   ]
  },
  {
   "source": [
    "# Hello World H2\n",
    "[source](https://github.com/aspuru-guzik-group/tequila-tutorials/blob/main/Chemistry.ipynb)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Optimizer: <class 'tequila.optimizers.optimizer_scipy.OptimizerSciPy'> \nbackend         : qulacs\ndevice          : None\nsamples         : None\nsave_history    : True\nnoise           : None\n\nMethod          : BFGS\nObjective       : 1 expectationvalues\ngradient        : 2 expectationvalues\n\nactive variables : 1\n\nE=-7.19702401  angles= {a: 0.0}  samples= None\nE=-7.19766373  angles= {a: -0.0234220027923584}  samples= None\nE=-7.20203763  angles= {a: -0.11711001396179199}  samples= None\nE=-7.24749228  angles= {a: -0.49186205863952637}  samples= None\nE=-7.68640044  angles= {a: -1.9908702373504639}  samples= None\nE=-7.59671569  angles= {a: -7.986902952194214}  samples= None\nE=-7.86212174  angles= {a: -3.1296004573374767}  samples= None\nE=-7.59889693  angles= {a: -1.7104055550334705}  samples= None\nE=-7.86268191  angles= {a: -3.0777237637496664}  samples= None\nE=-7.86268890  angles= {a: -3.0712456201159157}  samples= None\nOptimization terminated successfully.\n         Current function value: -7.862689\n         Iterations: 3\n         Function evaluations: 10\n         Gradient evaluations: 10\nVQE : -7.8626889f\nFCI : -7.8626889f\n"
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      "text/plain": [
       "[-7.862121739985106, -7.8626819139001345, -7.862688897539101]"
      ]
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     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "import tequila as tq\n",
    "active_orbitals = {'A1':[1], 'B1':[0]}\n",
    "molecule = tq.chemistry.Molecule(geometry='H 0.0 0.0 0.0\\nLi 0.0 0.0 1.6', basis_set='sto-3g', active_orbitals=active_orbitals)\n",
    "hamilton = molecule.make_hamiltonian()\n",
    "\n",
    "# Define a hand designed circuit.\n",
    "U = tq.gates.Ry(angle='a', target=0) + tq.gates.X(target=[2,3])\n",
    "U += tq.gates.X(target=1, control=0)\n",
    "U += tq.gates.X(target=2, control=0)\n",
    "U += tq.gates.X(target=3, control=1)\n",
    "\n",
    "expectationvalue = tq.ExpectationValue(H=hamilton, U=U)\n",
    "\n",
    "# Optimize.\n",
    "result = tq.minimize(objective=expectationvalue, method='BFGS', initial_values={k:0.0 for k in expectationvalue.extract_variables()})\n",
    "\n",
    "# Compute a reference value with psi4\n",
    "fci = molecule.compute_energy(method='fci')\n",
    "\n",
    "print(\"VQE : {:+2.8}f\".format(result.energy))\n",
    "print(\"FCI : {:+2.8}f\".format(fci));\n",
    "vars(result)\n",
    "result.history.energies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# More information from the optimization\n",
    "result.history.plot('energies', baseline={'fci':fci})"
   ]
  },
  {
   "source": [
    "# Prototype function for benchmarking (tequila)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tequila as tq\n",
    "def benchmark(molecule, circuit, method='BFGS'):\n",
    "    E = tq.ExpectationValue(H=molecule.make_hamiltonian(), U=circuit)\n",
    "    result = tq.minimize(objective=E, method=method, initial_values={k:0.0 for k in E.extract_variables()}, silent=True)\n",
    "    fci = molecule.compute_energy(method='fci')\n",
    "    return {\n",
    "        'accuracy': abs(result.energy - fci),\n",
    "        'iterations': len(result.history.energies),\n",
    "        'measurements': 'unknown',\n",
    "        'gate_depth': 'somefuncsomething'\n",
    "    }"
   ]
  },
  {
   "source": [
    "## Test 1"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'accuracy': 2.7107205369247822e-12,\n",
       " 'iterations': 3,\n",
       " 'measurements': 'unknown',\n",
       " 'gate_depth': ''}"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "# Molecule\n",
    "active_orbitals = {'A1':[1], 'B1':[0]}\n",
    "molecule = tq.chemistry.Molecule(geometry='H 0.0 0.0 0.0\\nLi 0.0 0.0 1.6', basis_set='sto-3g', active_orbitals=active_orbitals)\n",
    "\n",
    "# Circuit\n",
    "U = tq.gates.Ry(angle='a', target=0) + tq.gates.X(target=[2,3])\n",
    "U += tq.gates.X(target=1, control=0)\n",
    "U += tq.gates.X(target=2, control=0)\n",
    "U += tq.gates.X(target=3, control=1)\n",
    "\n",
    "benchmark(molecule, U)"
   ]
  },
  {
   "source": [
    "# Sandbox"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'_n_qubits': None,\n",
       " '_min_n_qubits': 0,\n",
       " '_gates': [Ry(target=(0,), parameter=a),\n",
       "  X(target=(2, 3), control=()),\n",
       "  X(target=(1,), control=(0,)),\n",
       "  X(target=(2,), control=(0,)),\n",
       "  X(target=(3,), control=(1,))],\n",
       " '_parameter_map': defaultdict(list, {a: [(0, Ry(target=(0,), parameter=a))]})}"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "U = tq.gates.Ry(angle='a', target=0) + tq.gates.X(target=[2,3])\n",
    "U += tq.gates.X(target=1, control=0)\n",
    "U += tq.gates.X(target=2, control=0)\n",
    "U += tq.gates.X(target=3, control=1)\n",
    "\n",
    "vars(U)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[a]"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "import tequila as tq\n",
    "active_orbitals = {'A1':[1], 'B1':[0]}\n",
    "molecule = tq.chemistry.Molecule(geometry='H 0.0 0.0 0.0\\nLi 0.0 0.0 1.6', basis_set='sto-3g', active_orbitals=active_orbitals)\n",
    "hamilton = molecule.make_hamiltonian()\n",
    "\n",
    "# Define a hand designed circuit.\n",
    "U = tq.gates.Ry(angle='a', target=0) + tq.gates.X(target=[2,3])\n",
    "U += tq.gates.X(target=1, control=0)\n",
    "U += tq.gates.X(target=2, control=0)\n",
    "U += tq.gates.X(target=3, control=1)\n",
    "\n",
    "E = tq.ExpectationValue(H=hamilton, U=U)\n",
    "\n",
    "E.extract_variables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}